Deep Sketch Hashing: Fast Free-hand Sketch-Based Image Retrieval

Free-hand sketch-based image retrieval (SBIR) is a specific cross-view retrieval task, in which queries are abstract and ambiguous sketches while the retrieval database is formed with natural images. Work in this area mainly focuses on extracting representative and shared features for sketches and natural images. However, these can neither cope well with the geometric distortion between sketches and images nor be feasible for large-scale SBIR due to the heavy continuous-valued distance computation. In this paper, we speed up SBIR by introducing a novel binary coding method, named \textbf{Deep Sketch Hashing} (DSH), where a semi-heterogeneous deep architecture is proposed and incorporated into an end-to-end binary coding framework. Specifically, three convolutional neural networks are utilized to encode free-hand sketches, natural images and, especially, the auxiliary sketch-tokens which are adopted as bridges to mitigate the sketch-image geometric distortion. The learned DSH codes can effectively capture the cross-view similarities as well as the intrinsic semantic correlations between different categories. To the best of our knowledge, DSH is the first hashing work specifically designed for category-level SBIR with an end-to-end deep architecture. The proposed DSH is comprehensively evaluated on two large-scale datasets of TU-Berlin Extension and Sketchy, and the experiments consistently show DSH's superior SBIR accuracies over several state-of-the-art methods, while achieving significantly reduced retrieval time and memory footprint.Comment: This paper will appear as a spotlight paper in CVPR201

One stone, two birds: A lightweight multidimensional learned index with cardinality support

Innovative learning based structures have recently been proposed to tackle index and cardinality estimation tasks, specifically learned indexes and data driven cardinality estimators. These structures exhibit excellent performance in capturing data distribution, making them promising for integration into AI driven database kernels. However, accurate estimation for corner case queries requires a large number of network parameters, resulting in higher computing resources on expensive GPUs and more storage overhead. Additionally, the separate implementation for CE and learned index result in a redundancy waste by storage of single table distribution twice. These present challenges for designing AI driven database kernels. As in real database scenarios, a compact kernel is necessary to process queries within a limited storage and time budget. Directly integrating these two AI approaches would result in a heavy and complex kernel due to a large number of network parameters and repeated storage of data distribution parameters. Our proposed CardIndex structure effectively killed two birds with one stone. It is a fast multidim learned index that also serves as a lightweight cardinality estimator with parameters scaled at the KB level. Due to its special structure and small parameter size, it can obtain both CDF and PDF information for tuples with an incredibly low latency of 1 to 10 microseconds. For tasks with low selectivity estimation, we did not increase the model's parameters to obtain fine grained point density. Instead, we fully utilized our structure's characteristics and proposed a hybrid estimation algorithm in providing fast and exact results

Experimental investigation on shock mechanical properties of red sandstone under preloaded 3D static stresses

Triaxial impact mechanical performance experiment was performed to study the mechanical properties of red sandstone subjected to three-dimensional (3D) coupled static and dynamic loads, i.e., three confining pressures (0, 5, and 10 MPa) and three axial pressures (11, 27, and 43 MPa). A modified 3D split Hopkinson pressure bar testing system was used. The change trend in the deformation of red sandstone and the strength and failure modes under axial pressures and confining pressures were analyzed. Results show that, when the confining pressure is constant, the compressive strength, secant modulus, and energy absorbed per unit volume of red sandstone initially increases and subsequently decreases, whereas the average strain rate exhibits an opposite trend. When the axial pressure is constant, both the compressive strength and secant modulus of red sandstone are enhanced, but the average strain rate is decreased with increasing confining pressure. The energy absorbed per unit volume is initially increased and subsequently decreased as the confining pressure increases. Red sandstone exhibits a cone-shaped compression–shear failure mode under the 3D coupled static and dynamic loads. The conclusions serve as theoretical basis on the mechanical properties of deep medium-strength rock under a high ground stress and external load disturbance condition

Genetic diversity of five goat breeds in China based on microsatellite markers

The genetic diversity of five goat breeds in China was surveyed using 15 microsatellites. The five goat breeds included Tangshan dairy goat (TSD), Liaoning cashmere goat (LNC), Nanjiang yellow goat (NJY), Chengde  polled goat (CDP) and Leizhou black goat (LZB). The mean polymorphism information content value (PIC) of  the populations ranged from 0.6606 to 0.8405. The mean heterozygosity (H) of the populations ranged from  0.7936 to 0.8202. The mean number of effective allele (Ne) of the populations ranged from 5.3373 to 5.8812 and the coefficient of genetic differentiation between breeds was 0.0620. It was suggested that the five goat  populations have abundant genetic diversity and extensive genetic basis, with limited inbreeding, especially in Leizhou black goat. The unweighted pair-group method with arithmetic averages (UPGMA) dendrogram based  on the Nei's standard genetic distance indicated that Tangshan dairy goat, Chengde polled goat and Liaoning  cashmere goat breeds / populations clustered together. The Nanjiang yellow goat and Leizhou black goat  populations clustered together, consistent with the geographical distribution of goat breeds.Key words: Goat, microsatellite, genetic diversity, clustering